2017 Brewers Minors: Bats

The Milwaukee Brewers minor league affiliates completed their 2017 campaigns on Sunday, in the form of a gutwrenching Game Five Colorado Springs loss in the Class-AAA Pacific Coast League playoffs. Now that the minor league season is over, fans and analysts can begin to process information about the season, including scouting reports and statistical analysis to find the system’s best players or overlooked depth options (and, honestly, probably anyone in between).

There are methodological shortcomings to both scouting and statistical reports. Scouting reports have shortcomings in terms of potential observer biases or preferences, limited looks (even some organizations draft a player after only one look), and privileged information (or, at times, even intentional asymmetrical information from clandestine “sources”); statistics are impacted by all the contextual factors present at the MLB level, but perhaps even moreso age, quality of competition, developmental cycle (i.e., first trip to a level), and proprietary development assignments that will typically be unknown to external observers. These factors diminish the meaning of minor league statistis.

BPMilwaukee benefits from work with the Baseball Prospectus scouting team, which consistently uses a radical “eyes in the field” approach to drive scouting reports that are therefore often divergent from, and perhaps less hype-worthy (a good thing), than many industry competitors. (One example here would be the 2016 approach to RHPs Brandon Woodruff and Phil Bickford, compared to sources like FanGraphs and BaseballAmerica). To supplement reports, which can be gathered from BP player pages and daily prospect summaries published on the website, statistical analysis can be applied to index contextual factors that could impact the perception of a player’s performance. To this end, I will publish a two part series detailing the contextual factors impacting Brewers regular (or semiregular) batting and pitching minor leaguers, which should hopefully add information to the use of scouting lines throughout the offseason.

I choose the method of indexing statistics because it is relatively straightforward in terms of user interface: every metric can be measured against a “constant” or comparison variable, such as “Player Total Average (TAv)” versus “League TAv.” In an index, 1.00 can basically be read as “average,” for it means that a player’s metric perfectly matches the comparison metric. For example, Outfield Michael Reed played during his age-24 season at Class-AA Biloxi, and the Southern League had a median age of 24; therefore, Reed’s age index is 1.00 (after all these years, Reed is still not “old” for advanced minor league ball).

For system wide reference, here is a key:

Median (50+ PA)

Players

TAV

oppOPS

Park

Age

Pacific Coast (AAA)

324

0.265

0.772

101

26

Southern (AA)

195

0.252

0.686

99

24

Carolina (Advanced A)

187

0.260

0.700

98

23

Midwest (A)

324

0.259

0.701

102

22

For this exercise, I indexed Age, TAv, Opposing OPS (oppOPS), and Park Factor statistics drawn from Baseball Prospectus CSV (retrieved September 13, 2017). These categories do not exhaust the information available, but they are arguably foundations for measuring the typical quality of the regular players in the league, the quality of opposing pitchers, and any extreme (or not) park environments. Note that I did not focus on Rookie classes (Pioneer League, Arizona League, or Dominican Summer League), as those leagues are not only (arguably) more instructional in nature but also representative of professionals at their earliest stages of development (therefore, I am not quite certain what Rookie class stats “say”).

I used two methods:

Once I created an Index for each of these statistics, I weighted each player’s OPS by assessing it against the Opposing OPS quality and contextual factors. Again, 1.00 can be read as average; below 1.00 can be read as below average, while above 1.00 can be read as better than average.

To provide a quality control for this rudimentary method, I used a basic TAv Index. Since Total Average is already scaled to many contextual factors, it more accurately reflects a player’s performance within a specific run, park, and league environment (certainly moreso than OPS).

By using a 50 Plate Appearance cut off, I captured 72 batting seasons performed by 63 Brewers minor leaguers.

Index

Team

PA

TAV

OPS

oppOPS

oppIndex

AgeIndex

ParkIndex

WeightedOPS

TAVIndex

Keston Hiura

WIS

115

0.326

0.850

0.694

0.99

0.91

0.99

1.37

1.26

Troy Stokes

BLX

153

0.279

0.785

0.676

0.99

0.88

1.01

1.33

1.11

Monte Harrison

CAR

252

0.305

0.828

0.696

0.99

0.91

1.02

1.28

1.17

Monte Harrison

WIS

261

0.307

0.834

0.698

1.00

0.95

1.00

1.26

1.19

Jake Gatewood

BLX

100

0.258

0.757

0.691

1.01

0.88

1.01

1.23

1.02

Troy Stokes

CAR

426

0.289

0.789

0.697

1.00

0.91

1.04

1.20

1.11

Garrett Cooper

CSP

320

0.329

1.080

0.785

1.02

1.00

1.15

1.18

1.24

Brett Phillips

CSP

432

0.295

0.944

0.779

1.01

0.88

1.16

1.17

1.11

Jake Gatewood

CAR

470

0.281

0.779

0.704

1.01

0.91

1.04

1.16

1.08

Weston Wilson

WIS

162

0.313

0.842

0.719

1.03

1.00

1.00

1.14

1.21

Jacob Nottingham

BLX

385

0.263

0.695

0.677

0.99

0.92

1.00

1.13

1.04

Mario Feliciano

WIS

446

0.246

0.651

0.702

1.00

0.82

1.00

1.13

0.95

Mauricio Dubon

BLX

304

0.245

0.689

0.674

0.98

0.92

1.01

1.12

0.97

Mitch Ghelfi

CAR

61

0.309

0.789

0.665

0.95

1.04

1.07

1.12

1.19

Cooper Hummel

CAR

239

0.269

0.749

0.691

0.99

0.96

1.03

1.11

1.03

Clint Coulter

BLX

437

0.271

0.721

0.681

0.99

0.96

1.00

1.11

1.08

Wendell Rijo

CAR

137

0.275

0.758

0.709

1.01

0.91

1.04

1.11

1.06

Trent Clark

CAR

569

0.263

0.708

0.703

1.00

0.87

1.04

1.11

1.01

Dallas Carroll

WIS

127

0.280

0.752

0.679

0.97

1.05

1.00

1.09

1.08

Lewis Brinson

CSP

340

0.299

0.962

0.798

1.03

0.88

1.21

1.09

1.13

Michael Choice

BLX

195

0.315

0.852

0.698

1.02

1.13

0.98

1.09

1.25

Ronnie Gideon

WIS

438

0.260

0.714

0.687

0.98

1.00

1.00

1.06

1.01

Michael Reed

BLX

205

0.267

0.698

0.666

0.97

1.00

1.02

1.06

1.06

Javier Betancourt

BLX

361

0.235

0.653

0.681

0.99

0.92

1.00

1.05

0.93

Isan Diaz

CAR

455

0.263

0.710

0.702

1.00

0.91

1.05

1.05

1.01

Rene Garcia

BLX

52

0.315

0.807

0.689

1.00

1.13

0.99

1.05

1.25

Tucker Neuhaus

WIS

355

0.273

0.749

0.708

1.01

1.00

1.01

1.04

1.06

Dustin DeMuth

BLX

433

0.263

0.719

0.677

0.99

1.04

1.00

1.03

1.04

Lucas Erceg

CAR

538

0.259

0.724

0.705

1.01

0.96

1.04

1.02

1.00

Blake Allemand

BLX

370

0.263

0.695

0.690

1.01

1.00

0.98

1.02

1.04

Demi Orimoloye

WIS

518

0.239

0.632

0.696

0.99

0.91

1.00

1.01

0.92

Corey Ray

CAR

503

0.255

0.679

0.698

1.00

0.96

1.03

0.99

0.98

Ryan Cordell

CSP

292

0.270

0.855

0.774

1.00

0.96

1.17

0.98

1.02

Angel Ortega

BLX

503

0.236

0.629

0.682

0.99

0.96

1.00

0.97

0.94

Tyrone Taylor

BLX

95

0.248

0.692

0.709

1.03

0.96

1.02

0.97

0.98

Rene Garcia

CSP

127

0.279

0.836

0.761

0.99

1.04

1.13

0.95

1.05

Nathan Orf

CSP

507

0.288

0.904

0.775

1.00

1.04

1.18

0.95

1.09

Jose Cuas

WIS

203

0.238

0.646

0.677

0.97

1.05

1.00

0.95

0.92

Dustin Houle

BLX

163

0.232

0.620

0.690

1.01

0.96

1.00

0.93

0.92

Weston Wilson

CAR

288

0.234

0.624

0.688

0.98

0.96

1.04

0.93

0.90

Zach Clark

WIS

108

0.241

0.612

0.700

1.00

0.95

0.99

0.93

0.93

Caleb Whalen

WIS

101

0.252

0.675

0.685

0.98

1.09

1.00

0.92

0.97

Trever Morrison

WIS

345

0.228

0.619

0.686

0.98

1.00

1.00

0.92

0.88

Jett Bandy

CSP

51

0.265

0.912

0.792

1.03

1.04

1.19

0.91

1.00

Nathan Rodriguez

WIS

182

0.228

0.563

0.678

0.97

0.95

1.01

0.89

0.88

Mauricio Dubon

CSP

244

0.229

0.739

0.793

1.03

0.85

1.21

0.89

0.86

Carlos Belonis

CAR

112

0.228

0.623

0.701

1.00

0.96

1.05

0.88

0.88

Johnny Davis

BLX

553

0.248

0.656

0.679

0.99

1.13

1.00

0.87

0.98

Max McDowell

CAR

306

0.244

0.629

0.702

1.00

1.00

1.04

0.86

0.94

Joantgel Segovia

WIS

444

0.191

0.516

0.686

0.98

0.91

1.00

0.85

0.74

Luis Aviles

CAR

529

0.215

0.585

0.698

1.00

0.96

1.04

0.84

0.83

Ivan De Jesus

CSP

466

0.280

0.894

0.773

1.00

1.15

1.20

0.84

1.06

Devin Hairston

WIS

177

0.225

0.552

0.697

0.99

0.95

1.00

0.83

0.87

Tyler Heineman

CSP

228

0.251

0.749

0.764

0.99

1.00

1.19

0.83

0.95

Wendell Rijo

BLX

91

0.226

0.497

0.695

1.01

0.88

0.97

0.83

0.90

Kyle Wren

CSP

540

0.257

0.766

0.777

1.01

1.00

1.18

0.83

0.97

Ryan Aguilar

WIS

409

0.228

0.574

0.696

0.99

1.00

1.00

0.83

0.88

Eric Sogard

CSP

107

0.294

0.937

0.787

1.02

1.19

1.19

0.82

1.11

Gilbert Lara

WIS

234

0.184

0.496

0.701

1.00

0.86

1.01

0.81

0.71

Gabriel Noriega

BLX

140

0.190

0.557

0.673

0.98

1.08

0.99

0.79

0.75

Nick Noonan

CSP

204

0.250

0.762

0.777

1.01

1.08

1.17

0.77

0.94

Mitch Ghelfi

WIS

65

0.226

0.558

0.685

0.98

1.09

1.00

0.76

0.87

Art Charles

BLX

122

0.211

0.535

0.674

0.98

1.08

1.00

0.75

0.84

Jose Cuas

CAR

139

0.216

0.530

0.697

1.00

1.00

1.03

0.74

0.83

Carlos Belonis

WIS

106

0.197

0.538

0.705

1.01

1.00

1.03

0.74

0.76

Jonathan Oquendo

WIS

114

0.162

0.442

0.665

0.95

0.95

1.00

0.73

0.63

Chris Colabello

CSP

183

0.292

0.887

0.791

1.02

1.27

1.18

0.73

1.10

Victor Roache

BLX

80

0.173

0.467

0.642

0.94

1.04

1.03

0.72

0.69

Kirk Nieuwenhuis

CSP

247

0.247

0.723

0.792

1.03

1.12

1.17

0.68

0.93

Andrew Susac

CSP

202

0.230

0.710

0.810

1.05

1.04

1.19

0.68

0.87

Yadiel Rivera

CSP

414

0.194

0.596

0.778

1.01

0.96

1.18

0.67

0.73

Gabriel Noriega

CSP

78

0.209

0.560

0.763

0.99

1.00

1.23

0.61

0.79

A few observations:

The very best statistical performances within the minor league system have varying degrees of scouting support. Keston Hiura, for example, was advertised as one of the best bats in the 2017 Draft (perhaps the most advanced college bat, even), and Hiura indeed scorched the Midwest League to the tune of a .326 TAv; he did so while being young for his league (during his first pro go-around), facing solid to slightly-tough opponents, and a moderate park environment. Jake Gatewood may have been the surprise breakout of the season, combining new contact lenses and mechanical advancements to pummel both Class-Advanced A and Class-AA leagues. Monte Harrison was another strong tools prospect – really, a fantastic athlete – that finally played a healthy season. Harrison and Gatewood diverge in terms of the type of Overall Future Potential roles they have, but here they converge in making great strides in showcasing their tools in 2017. On the other hand, Troy Stokes does not necessarily have the full scouting pedigree behind his statistical performance, which makes his 2017 season eye opening in terms of assessing an organizational depth role. Garrett Cooper was advanced minor league depth who went on to post a .275 TAv in 45 injury-shortened PA with the Yankees.

Much has been made of the disappointing season by the much-hyped gang of Carolina bats (Corey Ray, Lucas Erceg, Isan Diaz, and Trent Clark). However, it should be underscored that they did not actually have bad seasons. At worst, they had arguably average seasons when considering their age and developmental status (each facing a new league the first time through). However, what is interesting is that each player has new scouting reports on potential flaws that could indeed impact Overall Future Potential; for example, a midseason 2017 eyewitness report downgraded Ray’s role, and several other members of the prospect team confirmed hit tool concerns throughout the season. This type of scouting concern cycle was repeated for several of these prospects, but it is worth nothing this was also scouts’ first full look at Corey Ray and Lucas Erceg. In this case, one ought to hold the conclusions on statistical and scouting assessments for another season, as more information will be necessary to understand these potential shortcomings. Anyway, hold the hype (and really, be healthy about hype in the first place!).

There are a ton of interesting sleepers around here. Some interesting reports are floating around regarding infielder Wendell Rijo, for example, but the second baseman has never really flashed the stats. This year, Rijo graded solidly both in terms of contextual OPS and TAv. Clint Coulter remained young for his level (Class-AA Biloxi in 2017), and posted some intriguing peripheral statistics to go along with his overall solid line. Granted, there are few emerging reports on either of these players yet, which leaves room for a healthy dose of skepticism about future role. But, it’s worth remembering that many of these guys are so young when drafted that they remain young even through several repeated stages of Advanced ball development; hanging around at that upper level, one wonders what might come of a guy like Coulter after Garrett Cooper received trade interest.

In terms of melding scouting pedigree and performance, Brett Phillips might have the best season of any Brewers prospect. While many viewed his 2016 campaign as a disappointment, there were several aspects of Phillips’s game that exhibited strengths even through the perceived weaknesses of his stat line. Of course, Phillips was also quite young for his level. The intriguing “elite”-4th Outfielder-to-potential CF or RF starter put it all together in 2017, including a couple of stunning cups of coffee in the MLB (including a 2-for-4, three RBI night with an Outfield Assist against the Pirates on Wednesday). Hiura has the flashiest immediate hype and performance, but Phillips’s full season production and realization of one aspect of his MLB future could arguably win him “Best Bat” of the system for 2017. I gather that someone like Monte Harrison most deserves a “Player of the Year” Award, but Phillips should not be discounted when considering the Brewers system’s best players.